温度和湿度的物理约束深度学习后处理
Physics-constrained deep learning postprocessing of temperature and humidity
AI总结：
本文提出通过解析方程整合气象专业知识，在深度学习后处理模型中实现物理一致性，在不损害性能的前提下生成物理一致的温度和湿度预测，且在数据稀缺时优势显著。
摘要：
天气预报中心目前依赖统计后处理方法来最小化预报误差。这提高了预报技巧，但可能导致预测违反物理原理或忽略变量间的依赖关系，这对于下游应用和后处理模型的可信度是有问题的，特别是当它们基于新的机器学习方法时。基于物理信息机器学习的最新进展，我们提出通过以解析方程的形式整合气象专业知识，在基于深度学习的后处理模型中实现物理一致性。应用于瑞士地表天气后处理时，我们发现约束神经网络以执行热力学状态方程，能够在不损害性能的情况下产生物理一致的温度和湿度预测。我们的方法在数据稀缺时尤其有利，研究结果表明，将领域专业知识纳入后处理模型能够在满足特定应用需求的同时优化天气预报信息。
英文摘要：
Weather forecasting centers currently rely on statistical postprocessing methods to minimize forecast error. This improves skill but can lead to predictions that violate physical principles or disregard dependencies between variables, which can be problematic for downstream applications and for the trustworthiness of postprocessing models, especially when they are based on new machine learning approaches. Building on recent advances in physics-informed machine learning, we propose to achieve physical consistency in deep learning-based postprocessing models by integrating meteorological expertise in the form of analytic equations. Applied to the post-processing of surface weather in Switzerland, we find that constraining a neural network to enforce thermodynamic state equations yields physically-consistent predictions of temperature and humidity without compromising performance. Our approach is especially advantageous when data is scarce, and our findings suggest that incorporating domain expertise into postprocessing models allows to optimize weather forecast information while satisfying application-specific requirements.
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